Library / Artificial Intelligence
MCP Architecture: Production-Grade Model Context Protocol
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About this course
This course contains the use of artificial intelligence.
Your language models can reason. They cannot reach your systems — and every hand-built connector your teams write to bridge that gap multiplies cost, drift, and risk. The Model Context Protocol (MCP) replaces that tangle of one-off integrations with one open, governed standard: build the tool once, govern it once, use it from every agent you run.
This course teaches MCP the way production software deserves to be taught. You will start with the problem stated precisely — the integration explosion, why vendor function calling is a feature and not an architecture, and what interoperability actually buys an enterprise. Then you will open the protocol itself: the three roles (host, client, server), JSON-RPC 2.0 on the wire, the capability model of tools, resources, and prompts, and the full session lifecycle from initialize handshake to graceful shutdown.
From there, you build. You will write your first MCP server with production anatomy, design tool surfaces that models select correctly, write input schemas they can fill on the first attempt, and handle errors, idempotency, and long-running work honestly. You will expose resources and prompts as governed assets, choose between local stdio and remote streamable HTTP transports, pick deployment topologies, and connect MCP to the systems enterprises actually run: legacy platforms behind facades, databases without raw SQL exposure, REST and SaaS APIs, and document stores — with credentials kept out of models, prompts, and logs.
Then the course treats those connections as the attack surface they are. You will threat-model the estate, recognize tool poisoning and malicious servers, authenticate with OAuth 2.1, authorize per tool and per user, sandbox server processes, and watch a poisoned document try to steer an agent — before building the defense stack that stops it.
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Udemy: 2026-10-22 · Coursera: 2026-10-22 · O'Reilly: 2026-10-22 · 365 Data Science: 2026-10-22
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